Introduction of Anomaly Expansion Pack

The Anomaly Expansion Pack is a tool that detects abnormal data flow and immediately alerts users. For this detection, it uses prediction models built based on machine learning.

Basic principles

As shown below, Anomaly predicts an aggregate of the target data source in real time and monitors the actual value.

aggregation monitoring

Here, the value marked as Predict is the data aggregate predicted through machine learning, and the value marked as Actual is the actual monitored value. As shown below, the total abnormal score increases with the difference between the two values. That is, the data aggregate is considered as deviating from the normal range if the actual value is significantly different from the predicted value.

total abnormal score

In this example, a Moderate alarm is triggered when the abnormal score reaches 10, and a Critical alarm when the score reaches 80.

The alarms are reported through various channels to the user, so that immediate action can be taken in response to anomalies.

Key functions

The key functions of Anomaly are as follows:

Machine learning

User convenience enhanced with automatic recommendation of a prediction model based on machine learning

Alarms & reports

Immediate alarm triggering and report generation in case of anomaly

Analyze

Chart generation and analysis service linkable with Metatron Discovery

Linkage with learning system

New forms of analysis possible through linkage with external analytics systems

Structure

The menus in Anomaly are organized as follows:

metatron anomaly structure

Users can easily navigate across menus, use references to detailed items, and gain organic understanding of alarms including their rule settings, past occurrences, and overall statistics.